On the other hand, several modulation-based methods [1]}, [2]}, [3]} are proposed to enable controllable image restoration according to users' flavours. These modulation-based methods generally take the degradation level (or restoration strength) as a part of the network inputs, and then construct mapping between the reconstruction result and the degradation level during training.
During inference, users can simply adjust the value of the input degradation level, and the network will generate reconstruction results according to the corresponding restoration strength.
However, existing controllable image restoration methods can only be trained on datasets with simple degradation processes and known degradation types/levels.
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